Today's wireless networks suffer from capacity shortages due to the large proliferation of data-hungry devices like smart phones, tablets, and notebooks. The number of devices accessing the data-network is expected to increase at an exponential rate in the years to come. Even when the number of devices on the network begins to saturate, the applications driving the data demand will continue to grow. These applications include on-line gaming, video conferencing, high definition video, and file sharing and these applications vary in their latency and bandwidth requirements.
Even though integrated circuit technology continues to improve at an exponential rate following Moore's law, the spectrum scarcity makes it difficult for today's wireless networks to fully leverage the available computing power to cope with growing data demand. The effectiveness of multiple input, multiple output (MIMO) techniques are highly dependent on channel characteristics. For example, most outdoor channels, where most of the capacity crunch is taking place, lack the diversity necessary to deliver the capacity improvement promised by MIMO.
Many companies are focusing their attention on the vastly under-utilized bands at higher frequencies operating at millimeter-waves (mm-Wave) like 60 GHz as a potential for alleviating the capacity crunch, especially since most recent advances in silicon technology made radio transceivers at those bands very cost-effective. However, the poor propagation characteristics make it difficult to use these bands in access networks.
Multi-antenna beamforming technology with multiuser spatial beamforming offers a potential solution for alleviating the capacity crunch. Beamforming can significantly improve the spatial use in lower frequency bands and improve the propagation and coverage at higher frequency bands (e.g. mm-Wave). Unlike MIMO, the capacity gains are less dependent on the wireless environment. Multi-antenna wideband beamforming technology is sometimes referred to as space-time adaptive processing (STAP).
The performance of STAP improves with the number of antennas. However, the amount of processing and training required to find the optimal spatial filters (i.e. weights for each antenna) also grows with the number of antennas. The amount of processing can either be quadratic or cubic in the number of antennas depending on the desired accuracy. The amount of required training, also known as the time-bandwidth product, grows linearly with the number of antennas. The processing overhead can be reduced with the advancement of silicon technology. However, the training overhead reduces the effective throughput of the system and is independent of technology. If the wireless links are static, then the system has an infinite amount time to find the optimal weights. However, in practice, the channels change with time. The average time in which the channel remains relatively static is known as the coherent time. The ratio of the amount of the time required to train the adaptive antenna array (i.e. the time-bandwidth product) to the coherence time, represents the additional overhead. This overhead can somewhat be reduced by using the actual payload (i.e. transmitted data) for training in addition to known pilot symbols, but this also comes with some performance degradation and added latency.